Visual aid for identifying vertebral landmarks in ultrasound
Bibliographic record
Abstract
PURPOSE: Vertebral landmark identification with ultrasound is notoriously difficult. We propose to assist the user in identifying vertebral landmarks by overlaying a visual aid in the ultrasound image space during the identification process. METHODS: The operator first identifies a few salient landmarks. From those, a generic healthy spine model is deformably registered to the ultrasound space and superimposed on the images, providing visual aid to the operator in finding additional landmarks. The registration is re-computed with each identified landmark. A spatially tracked ultrasound system and associated software were developed. To evaluate the system, six operators identified vertebral landmarks using ultrasound images, and using ultrasound images paired with 3D spine visualizations. Operator performance and inter-operator variability were analyzed. Software usability was assessed following the study, through questionnaire. RESULTS: In assessing the effectiveness of 3D spine visualization in landmark identification, operators were significantly more successful in landmark identification using visualizations and ultrasound than with ultrasound only (82 [72 – 94] % vs 51 [37 – 67] %, respectively; p = 0.0012). Time to completion was higher using visualizations and ultrasound than with ultrasound only 842 [448 – 1136] s vs 612 [434 – 785] s, respectively; p = 0.0468). Operators felt that 3D visualizations helped them identify landmarks, and visualize the spine and vertebrae. CONCLUSION: A three-dimensional visual aid was developed to assist in vertebral landmark identification using a tracked ultrasound system by deformably registering and visualizing a healthy spine model in ultrasound space. Operators found the visual aids useful and they were able to identify significantly more vertebral landmarks than without it.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".